Wafer inspection systems help a semiconductor manufacturer increase and maintain integrated circuit (IC) chip yields by detecting defects that occur during the manufacturing process. One purpose of inspection systems is to monitor whether a manufacturing process meets specifications. The inspection system indicates the problem and/or the source of the problem if the manufacturing process is outside the scope of established norms, which the semiconductor manufacturer can then address.
Evolution of the semiconductor manufacturing industry is placing ever greater demands on yield management and, in particular, on metrology and inspection systems. Critical dimensions are shrinking while wafer size is increasing. Economics is driving the industry to decrease the time for achieving high-yield, high-value production. Thus, minimizing the total time from detecting a yield problem to fixing it determines the return-on-investment for the semiconductor manufacturer.
Previous techniques for classification of defects, including manual classification and layer-based automatic classification, involve too much time and effort. As devices become more complex, manual classification of defects in semiconductor fabrication facilities requires increasing time and effort. Even after investing significant time for classification, defect classification can be inaccurate and inconsistent due to human errors. Current techniques of auto-classification of defects in field require many examples of defects and sometimes also human resources for training the classifiers. Furthermore, training the classifier for each defect type for every layer can be cumbersome, as a total number of classifiers to be trained will be the number of defect types times the number of layers.
Manual classification involves manual observation of each defect image with multiple perspectives, defect identification with a known set of reference defect images, and manual allocation of class codes for each defect site. Manual classification of defects requires significant time to complete. This, in turn, is very expensive. Furthermore, use of human judgment during classification can introduce inaccuracies and inconsistencies in the results.
Layer-based automatic classification includes custom classifiers separating all of the critical defect types present that are created for each layer. Classifiers can be created either manually or automatically. Layer-based automatic classification implements creation of layer-specific custom classifiers. The training of classifiers for all the layers requires extensive resources like training data, human resources, and time. For example, training of a classifier for each layer requires training data. The training data should have enough defect examples for each critical defect required to be classified by the classifier. Some classifier creation schemes being used in the field require manual classifier creation. Along with involving significant time investment, this also brings in the inconsistency in performance of created classifiers due to inaccuracy in judging the best set of attributes for creating those classifiers. Large amounts of time are spent on creating, training, and maintaining the classifiers with huge repetitions in their creation for same defect types across layers at a particular site and also across multiple customer sites.
Therefore, what is needed is a system and technique that reduces time and effort required to classify wafer defects.